The current consumer-oriented retail industry increasingly depends on the information about consumers' needs and their behavior in the retail environment—what they want to buy, in what way they are satisfied, and how they make purchase decisions. There seems to be highly-complex interplay of various factors in the shoppers' perceptions of products and their decision making processes. While it is unfeasible to fully understand consumer behavior, certain consumer behavior does reveal their mental process toward purchase decisions.
The widespread use of video cameras in stores and advancements of video analysis technology have made it possible to extract shopper behavioral data from in-store videos. Shoppers can be tracked within each field of view of cameras, and also across multiple camera views. Utilizing video processing and artificial intelligence technology, customer interaction with products can be analyzed and recognized to some extent. This kind of analysis provides a rich source of information from which many current state-of-the-art statistical analyses and data mining techniques can be used to extract useful data for marketers or retailers.
On the other hand, retail stores keep vast amounts of point-of-sale data—the lists of purchased products along with customer IDs. While the POS data itself provides valuable information such as customer loyalty data, cross-shopping data, etc., it fails to provide any additional aspects of shopper behavior, for example, how the shoppers interact with products and choose to purchase products. The addition of shopper behavior data matched to the POS data provides a missing piece in the puzzle of understanding customer behavior in a retail space. However, because it is not cost-effective or sometimes not physically feasible to cover the whole store floor, in-store videos typically do not provide matches between the tracked customers and the checking out customers. Under this scenario, a systematic strategy is necessary to make correspondences based on available data.
The present invention provides a framework and means to find correspondences between the POS data and the customer behavior data by identifying which person at the checkout caused which purchase events and behavioral cues detected from point-of-purchase videos. In a typical scenario, the POS (point-of-sale) data is available—the identity of each of the checking out shoppers along with the list of the items purchased. The behavior data of shoppers is estimated from an automatic analysis of videos collected from store aisles or end caps. The behavior data is typically generated in the form of purchase events with estimated purchase items, and the track of the shopper in the area covered by the view of the videos. Under this scenario, the complete shopping track of the customer is not available, due to the cost or physical constraints of camera placements. Consequently, the correspondence between the customer identity from the POS data and the customer identity of the shopper in the behavior data is not given. Therefore, the present invention finds the correspondences between the checkout data (POS) and the shopping behavior data from videos. More specifically, given the POS data—the shopper, the time of the checkout, and the list of the purchase items—the corresponding purchase events observed from the point-of-purchase videos need to be identified.
There have been prior attempts for analyzing customer transaction data to further extract useful information. One method generates an identification code for each customer based on the identification provided by a shopper at checkout. The method associates all products purchased by that shopper to that identification code, and that purchase history is stored in a centralized database. Based on the purchase history, the method provides for a means to target sales promotions to those shoppers.
One method and system gathers and analyzes customer and purchasing information and transaction information corresponding to large numbers of consumers and consumer products. Consumers are grouped into clusters based on demographic information, and products are grouped into generic clusters. Consumer retail transactions are analyzed in terms of product and/or consumer clusters to determine relationships between the consumers and the products. Product, consumer, and transactional data are maintained in a relational database. A retailer queries the database using selected criteria, accumulates data from the database in response to that query, and makes prudent business and marketing decisions based on that response.
Another method discloses a customer behavior based on the time when those behaviors occur. This method collects information about customer transactions and interactions over time, classifies customers into one or more clusters based on their time-based interactions and transactions, or both. The customer interactions with products consist of web-browsing activity, response to call center surveys, and purchase history in store. The method temporally tags customer transactions and interactions, analyzes the tagged information to create temporal profiles, creates advertising campaigns aimed at the temporal profiles, triggers an advertising campaign, and analyzes the effectiveness of the advertising campaign.
One system anticipates consumer behavior and determines transaction incentives for influencing consumer behavior. The system comprises a computer system and associated date for determining cross time correlations between transaction behavior, for applying the fiction derived from the correlations to consumer records to predict future consumer behavior, and for deciding on transaction incentives to offer the consumers based upon their predicted behavior.
The above methods and systems attempt to deliver insight into consumer behavior that impacts shopper purchasing in retail environments. However, these methods use only the POS data to derive insight into customer purchase behavior. By relying on POS data, these methods fail to capture customer behavior at a key juncture in shopper decision making, the point of purchase where customers place the item in their shopping basket. The customer weighs multiple factors including price, nutrition content, and brand at shelf before placing the item in his/her basket. This kind of information is not available through the methods described above.
There have been prior attempts for tracking customers and measuring customer interactions with products for the purpose of understanding their behaviors.
One method gathers data on the at-shelf behavior of shoppers in a retail market. A scanner attached to a shopping basket detects the removal of an item from a shelf, the identity of the removed item, the placement of an item into a shopping basket, which may be the identical item removed from the shelf, and the identity of the inserted item. Repeated detection of this type of data for numerous items, and numerous shoppers, allows one to draw inferences about the shoppers, such as how often comparison shopping occurs. This type of detection measures specific responses to the actions of the shoppers.
Another method analyzes shopper behavior of a shopper within a shopping environment. The method determines product locations in a store, tracks the path of a shopper through the shopping environment via a wireless tracking system, and calculates a product-shopper proximity measure based at least in part on a physical distance of a shopper traveling along the shopping path from the position of the product.
A monitoring system and method uses a shopper's location information and behavior prior to approaching the checkout counter to detect fraud. A tracking mechanism tracks a shopper and merchandise as the shopper is shopping and generates a list of currently acquired items. At the point-of-sale, the list of currently acquired items is compared to the list of purchase items and any discrepancies are provided. Another method determines whether a product is removed from a display. The method utilizes RFID tags installed in products and RFID sensors installed in a store.
In the above systems and methods, special devices installed on shopping baskets/carts and/or attached to products are required to measure the shopping and purchase behavior of customers. Such infrastructure is a high-cost means for tracking customer behavior and fails to capture the detailed information possible through video.
There have been prior attempts for analyzing customer behavior in the retail environment based on video images.
One method collects video surveillance data received from multiple unaffiliated locations at which various items are offered for sale. Based on the content of the video surveillance data, consumer preference behavior is characterized with respect to at least some of the items. The method can also collect POS data among other datasets to provide context to retailers. The data is consolidated and can be made available for further analysis. However, while this method does collect POS data, it does not provide the means for associating the POS data with shopper behavior to provide further insight into the impact of shopper behavior on purchase decisions.
Another method describes a video monitoring process which includes a video analytics engine to process video obtained by a video camera and generates video primitives regarding the video. A user interface is used to define at least one activity of interest regarding an area being viewed, each activity of interest identifying at least one of a rule or a query regarding the area being viewed. An activity inference engine processes the generated video primitives based on each defined activity of interest to determine if an activity of interest occurred in the video. While this method defines shopper activities of interest to the retailer and other stakeholders, it fails to connect the shopper behavior to a larger context of sales and business performance.
There have been prior attempts for making correspondences between data based on the measured attributes, using a statistical analysis.
One method associates customer behavior with purchases. Customer behavior is measured through collection of flow line data. Flow line data is collected by deploying video sensors, tracking customer paths from entry to exit and collecting the customer behavior and timestamps during the customer path. A specific identification is associated with flow line data. Times of payment are also estimated and then associated with the flow line data belonging to a specific ID with closest timestamp. Another method associates the consumer behavior within a particular sub-area to the transaction data using time-stamped flow line data captured by video. Another method associates the trajectory of shoppers with corresponding transaction data. The method uses known video tracking methods to generate trajectories of persons within areas of interest. The method associates video of transactions with customer trajectories which are not necessarily complete trajectories throughout the store.
These methods require use of an operator to complete essential functions in the method including estimating time of payment, following the flow line data to monitor customer behavior, determining the terminal number at time of transaction, etc. The requirement of manual input makes the methods difficult to carry out in an actual retail setting with many customers simultaneously checking.
One method discloses an optimization method for product placement within a retail store. The method uses position identifying systems to track where products are stocked within the store and paths of customers. Customers can be identified using financial transaction or other data. Products are chosen for purchase by customers and identified in their basket using perhaps GPS receivers, and the locations of the chosen products within the retail space are associated with the customer paths. The method further uses data mining to analyze the spatial relationships between customers and products. Because the method does not use video for customer and product tracking, the method cannot detect granular behaviors which positioning information systems may fail to detect.
The present invention addresses key gaps in the prior art with respect association between retail POS data and shopper behavior data. While there have been prior attempts to extract useful marketing information from POS data, the present invention provides a unique feature in that it combines the POS data and shopper behavior data. There have also been prior attempts to measure in-store shopper behaviors, but they depend on special communication devices, such as RFID tags and sensors, to track customers throughout the store and to detect product purchases from store shelves. The present invention depends instead on behavior data measured from point-of-purchase videos.